{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import model\n",
    "import time\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-11-02</td>\n",
       "      <td>778.200012</td>\n",
       "      <td>781.650024</td>\n",
       "      <td>763.450012</td>\n",
       "      <td>768.700012</td>\n",
       "      <td>768.700012</td>\n",
       "      <td>1872400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-11-03</td>\n",
       "      <td>767.250000</td>\n",
       "      <td>769.950012</td>\n",
       "      <td>759.030029</td>\n",
       "      <td>762.130005</td>\n",
       "      <td>762.130005</td>\n",
       "      <td>1943200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>750.659973</td>\n",
       "      <td>770.359985</td>\n",
       "      <td>750.560974</td>\n",
       "      <td>762.020020</td>\n",
       "      <td>762.020020</td>\n",
       "      <td>2134800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>774.500000</td>\n",
       "      <td>785.190002</td>\n",
       "      <td>772.549988</td>\n",
       "      <td>782.520020</td>\n",
       "      <td>782.520020</td>\n",
       "      <td>1585100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>783.400024</td>\n",
       "      <td>795.632996</td>\n",
       "      <td>780.190002</td>\n",
       "      <td>790.510010</td>\n",
       "      <td>790.510010</td>\n",
       "      <td>1350800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2016-11-02  778.200012  781.650024  763.450012  768.700012  768.700012   \n",
       "1  2016-11-03  767.250000  769.950012  759.030029  762.130005  762.130005   \n",
       "2  2016-11-04  750.659973  770.359985  750.560974  762.020020  762.020020   \n",
       "3  2016-11-07  774.500000  785.190002  772.549988  782.520020  782.520020   \n",
       "4  2016-11-08  783.400024  795.632996  780.190002  790.510010  790.510010   \n",
       "\n",
       "    Volume  \n",
       "0  1872400  \n",
       "1  1943200  \n",
       "2  2134800  \n",
       "3  1585100  \n",
       "4  1350800  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('GOOG-year.csv')\n",
    "date_ori = pd.to_datetime(df.iloc[:, 0]).tolist()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "minmax = MinMaxScaler().fit(df.iloc[:, 3].values.reshape((-1,1)))\n",
    "close_normalize = minmax.transform(df.iloc[:, 3].values.reshape((-1,1))).reshape((-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_normalize.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class encoder:\n",
    "    def __init__(self, input_, dimension = 2, learning_rate = 0.01, hidden_layer = 256, epoch = 20):\n",
    "        input_size = input_.shape[1]\n",
    "        self.X = tf.placeholder(\"float\", [None, input_.shape[1]])\n",
    "    \n",
    "        weights = {\n",
    "        'encoder_h1': tf.Variable(tf.random_normal([input_size, hidden_layer])),\n",
    "        'encoder_h2': tf.Variable(tf.random_normal([hidden_layer, dimension])),\n",
    "        'decoder_h1': tf.Variable(tf.random_normal([dimension, hidden_layer])),\n",
    "        'decoder_h2': tf.Variable(tf.random_normal([hidden_layer, input_size])),\n",
    "        }\n",
    "    \n",
    "        biases = {\n",
    "        'encoder_b1': tf.Variable(tf.random_normal([hidden_layer])),\n",
    "        'encoder_b2': tf.Variable(tf.random_normal([dimension])),\n",
    "        'decoder_b1': tf.Variable(tf.random_normal([hidden_layer])),\n",
    "        'decoder_b2': tf.Variable(tf.random_normal([input_size])),\n",
    "        }\n",
    "    \n",
    "        first_layer_encoder = tf.nn.sigmoid(tf.add(tf.matmul(self.X, weights['encoder_h1']), biases['encoder_b1']))\n",
    "        self.second_layer_encoder = tf.nn.sigmoid(tf.add(tf.matmul(first_layer_encoder, weights['encoder_h2']), biases['encoder_b2']))\n",
    "        first_layer_decoder = tf.nn.sigmoid(tf.add(tf.matmul(self.second_layer_encoder, weights['decoder_h1']), biases['decoder_b1']))\n",
    "        second_layer_decoder = tf.nn.sigmoid(tf.add(tf.matmul(first_layer_decoder, weights['decoder_h2']), biases['decoder_b2']))\n",
    "        self.cost = tf.reduce_mean(tf.pow(self.X - second_layer_decoder, 2))\n",
    "        self.optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(self.cost)\n",
    "        self.sess = tf.InteractiveSession()\n",
    "        self.sess.run(tf.global_variables_initializer())\n",
    "        \n",
    "        for i in range(epoch):\n",
    "            last_time = time.time()\n",
    "            _, loss = self.sess.run([self.optimizer, self.cost], feed_dict={self.X: input_})\n",
    "            if (i + 1) % 10 == 0:\n",
    "                print('epoch:', i + 1, 'loss:', loss, 'time:', time.time() - last_time)\n",
    "    \n",
    "    def encode(self, input_):\n",
    "        return self.sess.run(self.second_layer_encoder, feed_dict={self.X: input_})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 10 loss: 0.150638 time: 0.0008144378662109375\n",
      "epoch: 20 loss: 0.0652009 time: 0.0007433891296386719\n",
      "epoch: 30 loss: 0.0556741 time: 0.0007736682891845703\n",
      "epoch: 40 loss: 0.0430575 time: 0.0007085800170898438\n",
      "epoch: 50 loss: 0.0287333 time: 0.0006804466247558594\n",
      "epoch: 60 loss: 0.0152949 time: 0.0007014274597167969\n",
      "epoch: 70 loss: 0.0436488 time: 0.0006766319274902344\n",
      "epoch: 80 loss: 0.0830372 time: 0.0007102489471435547\n",
      "epoch: 90 loss: 0.0531746 time: 0.0007026195526123047\n",
      "epoch: 100 loss: 0.0455618 time: 0.0006778240203857422\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(252, 32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "Encoder=encoder(close_normalize.reshape((-1,1)), 32, 0.01, 128, 100)\n",
    "thought_vector = Encoder.encode(close_normalize.reshape((-1,1)))\n",
    "thought_vector.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import *\n",
    "ada = AdaBoostRegressor(n_estimators=500, learning_rate=0.1)\n",
    "bagging = BaggingRegressor(n_estimators=500)\n",
    "et = ExtraTreesRegressor(n_estimators=500)\n",
    "gb = GradientBoostingRegressor(n_estimators=500, learning_rate=0.1)\n",
    "rf = RandomForestRegressor(n_estimators=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada.fit(thought_vector[:-1, :], close_normalize[1:])\n",
    "bagging.fit(thought_vector[:-1, :], close_normalize[1:])\n",
    "et.fit(thought_vector[:-1, :], close_normalize[1:])\n",
    "gb.fit(thought_vector[:-1, :], close_normalize[1:])\n",
    "rf.fit(thought_vector[:-1, :], close_normalize[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RyRyDnohI5hj0REQyx6AnIpI5Bj0Rkcwx6ImIZI5BT0Qkcwx6IiKZY9ATEcmcXUGfm5sL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d2b075dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(32), ada.feature_importances_)\n",
    "plt.title('ada boost important feature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tmpub/dq0tLQgLy8PAGA0GtHe3g5ZlgEA27Ztw6RJk5CamhqC8omIKJCAH8Y6HA4kJib6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\nJDc0NMiyLMu//e1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d2a6b7048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(32), et.feature_importances_)\n",
    "plt.title('et important feature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JiATHoCciEhyDnohIcAx6IiLBMeiJiATHoCciEpxLQV9WVga9Xg+dTodNmzY123/48GEk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\nuP8HAO3gOLnfyZ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d2a3a2470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(32), gb.feature_importances_)\n",
    "plt.title('gb important feature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SUFNTg4yMDCiVSiQmJiIpKQlWqzU4IyEioi4pJEmSemrw3nvv4YMPPsCaNWsA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\nH0d5OA3KAcc6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d2ab3ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(32), rf.feature_importances_)\n",
    "plt.title('rf important feature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ada_pred=ada.predict(thought_vector)\n",
    "bagging_pred=bagging.predict(thought_vector)\n",
    "et_pred=et.predict(thought_vector)\n",
    "gb_pred=gb.predict(thought_vector)\n",
    "rf_pred=rf.predict(thought_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ada_actual = np.hstack([close_normalize[0],ada_pred[:-1]])\n",
    "bagging_actual = np.hstack([close_normalize[0],bagging_pred[:-1]])\n",
    "et_actual = np.hstack([close_normalize[0],et_pred[:-1]])\n",
    "gb_actual = np.hstack([close_normalize[0],gb_pred[:-1]])\n",
    "rf_actual = np.hstack([close_normalize[0],rf_pred[:-1]])\n",
    "stack_predict = np.vstack([ada_actual,bagging_actual,et_actual,gb_actual,rf_actual,close_normalize]).T\n",
    "corr_df = pd.DataFrame(stack_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O8+zT3Zn+/8YrHalKX+84yLrUo2RcuEyvP7Vh6ss9lY5k1fKETSQm77jZL4KZ\nPnG00pFuySQjcGWMmz6WGzduENmxHy3bteTDJdPJTD/JmRNnK7QbM2UU9Vzr0r/zQHzv9eGTFbPI\nycph48rNePp48s/F05n15sekbNzF01GhfLB4Gs8HvUTh1UJFvq+G3u4M79mJvcfOUlJmUCRDdfg1\n8GXEwH7s+f4gJaVlSsexScN7GzBySAy7U3+gVC2ZfdwZ3qsze9PPUnJDLf2iL3sOHKKkzHH/jmvT\nYlZVrruYlpZGWloaAJmZmSxatIjvvvvO7kHqudaj27NPsmDmYoqvl3D4wBF2b9lLz+jwSm2Dwruw\n/NMVlJaUkp2Vw4avv+HZmGcA6BDQjrzcPHYkpWAymUheu5UreVfp9syTds9sqzD/loR2bIG3ez3F\nMlTH008GEta1E95enkpHsVl492DCQoLw8fZSOorNwvxbEdqxJd4eaukXnQkLdvx+YarGS+2sjsDn\nzp1LSkoKBoOB4OBgDh06ROfOnYmLiyM9PZ3Y2Fi7BWna/H6MRiPnTmVZ9mUePYl/l463bK/5/W9J\nGmje5sHfHfvDr1AaaN72QYQQzs/0x3//TszqCHzz5s189dVXLFu2jGXLlvHpp58yevRoFixYwMaN\nG+0axNXdlaJrFZ8gUnStCDd310pt9397gIGjX8DV3ZX7HmxC7wHPUNe1LgBHfkingb4BYZFPodVp\n6dW/B/c1a0JdV3WMcoQQd8ZYjZfaWS3gWq0WrVaLq6srDzzwAB4eHgDUq1cPFxf7PvWiuKgYd0+3\nCvvcPN25XlRcqe0nk+ZSWlLKV7uW8P7CqWxN3M6li78AUJBfwNtD32XAiH6sO7iazt2f4PudP3Lp\n4iW75hVCOCaTxvaX2lmdQrnnnnsoLi7G1dWVtWvXWvZfu3bN7gX83KkstFot9z90H1mnzwPQ8pHm\nnD5+plLba1euMXXM+5btEW8O46eDxyzbB/elMaJ3+RlyrdaFFXuXsmLeKrvmFUI4ptp0FYrVKrxs\n2TJcXcunMH5fsG/cuME///lPuwYpKS4h5ZtdDJswhHqu9egQ0I6uPYLYvGZLpbZNmjXGy9cLFxcX\nOj/ViYiBvflizlLL8VbtWqLVaXHzcGPUpL+Se+ES+7/73q55q8NgNFF6w4DJZMZkNlN6w4DB6Lin\nUAxGI6VlZZhMJkwmE6VlZRiMjv0Lp8FgpLS0DKPRhNFkorS0DIPBwTP/vl+YTNIv7MRcjZfaWR2B\n16lT55aJJjxrAAAUt0lEQVT769evT/369e0eZvbbc3hr9husS1tNQX4Bs9+aw5kTZ3m0UwdmLn2f\nnq37ANDm0daMnTIKD28Pzp3KYuqrMypcavjiqAEEhnYCIHXHAd4eNtnuWavj8837mfdNqmV7w4Fj\njHymM7HPBiqY6vbilq7msyW//caStDWF2Jf7M2rwAAVTWTfvi6/4bOEyy3bS5u3EDh3I6GEvKZjK\nus83pTJv4z7L9oYDxxj5bCCxvbsomOr24pau4bMvV1u2k7buJHZQP0YNfl7BVJU5w9SIrTRms7lG\nfxA9eV9YTb693SUvilY6QrVpH1HuEsn/laaeu9IRqsVwMFnpCNWmbeOYPwisqdP01ledVcfi+2z/\noT3k/NKqGzkw+05kCyGEwowa219VSUlJoWfPnoSHhxMXF1fp+Pnz5xk8eDAREREMGjSI7Oxsy7GZ\nM2fSp08f+vTpU+GqvTfffJPQ0FAiIyOJjIzkp59+AiA1NZU//elPlv1z586tMp9D3YkphBB3yl5n\nEYxGI++99x6LFi1Cr9fTr18/QkNDadmypaXNBx98QFRUFM899xx79+5l9uzZzJw5kx07dpCenk5C\nQgJlZWUMGjSIkJAQy5V8EydOpFevXpU+MyAggHnz5tmcUUbgQginYq87MdPS0mjWrBlNmzalTp06\n9O7dm23btlVoc/LkSQIDy89lBQYGWo5nZmYSEBCATqfDzc2NNm3akJKSYr9v8iYp4EIIp2LW2P6y\nJicnh0aNGlm29Xo9OTk5Fdq0bduW5OTy8yNbtmyhqKiI/Px82rZty86dOykuLiYvL4/U1NQK0ysf\nf/wxERERzJgxg7LfrStz8OBB/vznPzN8+HAyMjKq/F6lgAshnMrdXAtl4sSJHDhwgKioKPbv349e\nr0er1dK1a1e6detGTEwM48ePx9/f33Ip9rhx49i0aRNr1qzh6tWrlrn1du3asX37dtatW8egQYMY\nPbrq1R6lgAshnIq9bqXX6/UVRs05OTno9fpKbebOnUtCQgKvv/46AF5e5QuqxcbGkpiYyKJFiwB4\n6KGHAPDz80Oj0VCnTh369u3L4cOHAfDw8MDdvfzqrG7dumEwGMjLy7OaUQq4EMKp2OtW+g4dOnDm\nzBnOnTtHWVkZGzZsIDQ0tEKbvLw8TKbysXxcXBzR0eWXIRuNRvLz8wE4duwYx48fJzg4GIDc3FwA\nzGYzW7dupVWrVgBcunSJX6/qTktLw2Qy4evrazWjXIUihHAq9roKRafTMWnSJIYPH47RaCQ6OppW\nrVoxZ84c2rdvT1hYGPv37+ejjz5Co9EQEBDA5MnlNw0aDAYGDhwIlI+sZ86ciU5XXm4nTJhAfn4+\nZrOZtm3b8o9//AP4bfFArVZLvXr1LO9rjdzI8wdyI8/dITfy1LzaeiPP7Adsv5Fn/M/qvpFHRuBC\nCKfiDGuc2EoKuBDCqdSmtVCkgAshnIrjrY9Yc2q8gE812H/Vwpqke7zy7a2OzrD+P0pHqL6mzZVO\nUC06/x5KR6g2Q+KnSkeovmF3PgduqkWTKDICF0I4FcddUd3+pIALIZxK7Rl/SwEXQjgZGYELIYRK\nGTS1ZwwuBVwI4VRqT/mWAi6EcDIyhSKEECollxEKIYRK1Z7yLQVcCOFkZApFCCFUyliLxuBSwIUQ\nTkVG4EIIoVJmGYELIYQ6yQjcgd03tBeNBnTH/eEHyI3fzbG//Z/Skaq0fPU6EjZuJePUaZ59ujvT\n/994pSNZ9fUPp1h3+GcyLl2j18P3MbXP40pHqtLXOw6yLvUoGRcu0+tPbZj6ck+lI1VJdf3ix9Os\nO3yOjF+u0evhJkx99jGlI92SXEbowEqz8zj7yRp8u/ujrVdH6Tg2aXhvA0YOiWF36g+UlpYpHadK\nDT3qMTyoDXtP51JyQx2rKzf0cWd4r87sTT9LyQ2D0nFsos5+0Yq9py9RYnDcflF7yrcKC/gvG/cD\n4NmxBdrGDRROY5vw7uVPoz56LIOc3F8UTlO1sDZNAEjPvkLJjWKF09gmzL/8yd7pP+dQkl+ocBrb\nqK5ftG4MQHr2VUquOW6/MNSiEu5S3S+YOHFiTeQQQgi7MFfjP7WzOgL/61//WmlfamqqZf9//qPC\nJ8EIIZyanMS8KScnhxYtWtC/f380Gg1ms5kjR44wdOjQu5VPCCGqxRlG1rayOoWyZs0a2rdvz3/+\n8x88PT3p3LkzdevWpVOnTnTq1OluZRRCCJuZqvFSO6sjcBcXF4YMGUKvXr2YMWMG9957L0ajsmef\nNVoXNDotaF1A64JL3XswG4yYjY77v8NgMGI0GjEaTRhNJkpLy9Bqteh0WqWj3ZLBZMJoMmMymTGZ\nzZQajGhdNOhcqn3K5K4x3Py7NZnMmEwmSm8Y0Lq4oNM6cGY19wuT4/YLo7n2jMBtugqlUaNG/Otf\n/2LHjh14eHjUdCarmr0ezYNvPG/ZbtQ/hDMzV3Jm1ioFU1k374uv+GzhMst20ubtxA4dyOhhLymY\n6vY+332CebuPW7Y3HM1iZHAbYp9sq2Aq6z7flMq8jfss2xsOHGPks4HE9u6iYCrrVNcv9mQwb88J\ny/aG9POMDGpNbNc2CqaqrDZdB64xm2v2x9UOff+afHu7Cz76gdIRqs2wXoUnk5s2VzpBtej8eygd\nodoMiZ8qHaHaXIfNuuP3eKFZlM1tvzqbcMefpyTVXQcuhBDWOO5kqv1JARdCOJXaNIUiBVwI4VRq\n02WEUsCFEE5FrkIRQgiVkikUIYRQKTmJKYQQKiVz4EIIoVIyhSKEECpVw/cmOhTHWsRACCHukBGz\nza+qpKSk0LNnT8LDw4mLi6t0/Pz58wwePJiIiAgGDRpEdna25djMmTPp06cPffr0YePGjZW+dtq0\naTz22G+PpSsrK+O1114jPDyc/v37k5WVVWU+KeBCCKdiwmzzyxqj0ch7773H/Pnz2bBhA0lJSWRm\nZlZo88EHHxAVFcX69esZNWoUs2fPBmDHjh2kp6eTkJDAypUrWbBgAYWFvz0p6vDhw1y9erXCe61a\ntQovLy+2bNnCkCFDmDWr6mUFpIALIZyK2Wy2+WVNWloazZo1o2nTptSpU4fevXuzbdu2Cm1OnjxJ\nYGAgAIGBgZbjmZmZBAQEoNPpcHNzo02bNqSkpADlPxg+/PBD3njjjQrvtX37dp577jkAevbsyd69\ne6vMWONz4E/n76npj7CrgrX/VjpCtd0TNUrpCNVmLr2udIRqUePCULpI9fULe7DXScycnBwaNWpk\n2dbr9aSlpVVo07ZtW5KTkxk8eDBbtmyhqKiI/Px82rZty9y5cxk6dCjFxcWkpqbSsmVLAJYuXUpY\nWBh+fn6VPq9x4/Lnjup0Ojw9PcnPz6d+/fq3zSgnMYUQTuVuXkY4ceJEpk6dSnx8PAEBAej1erRa\nLV27duXw4cPExMRQv359/P39cXFxIScnh02bNvHll1/a5fOlgAshnIq9bqXX6/UVTkrm5OSg1+sr\ntZk7dy4ARUVFJCcn4+XlBUBsbCyxsbEAjB8/noceeoiffvqJn3/+mR49ypcnLi4uJjw8nC1btqDX\n67l48SKNGjXCYDBw7do1fH19rWaUAi6EcCr2mkLp0KEDZ86c4dy5c+j1ejZs2GA5SfmrvLw8fHx8\ncHFxIS4ujujoaKB8nrugoABfX1+OHTvG8ePHCQ4ORqfTsXv3bsvXP/bYY2zZsgWA0NBQ4uPjeeyx\nx9i8eTOBgYFoNBqrGaWACyGcir0KuE6nY9KkSQwfPhyj0Uh0dDStWrVizpw5tG/fnrCwMPbv389H\nH32ERqMhICCAyZMnA2AwGBg4cCAAHh4ezJw5E53Oernt168fb7zxBuHh4Xh7e/Pxxx9XmbHGn8ij\nq3NfTb693RXMfb7qRg5GTmLWPGOyfeYs7yY1nsS85947f1JTYJPuNrfdd2HHHX+ekmQELoRwKnIr\nvRBCqJQsZiWEECplNNeeBWWlgAshnEptWsxKCrgQwqnIHLgQQqiUzIELIYRKmWQKRQgh1Kk2jcAd\najlZX18fVq+az9X8DE5mpBITE3XLdt7eXixc8AkXsg5xIesQk94dV+F4l8AA9u5OIv/ycX78YQvB\nQU/cjfi39fV/z/Di0l10+mQTkzYdUjSLLZavWc/zw1/jsdAo3ple9d1gjmB5wiYGjHqTx595kXc+\n/D+l49jk6x9P8+IXKTwxewPvbvyv0nGqtHz1Op4fOpbHukfwzrTZVX+BQoxmk80vtXOoEfi//zWd\nsrIbNLm/I/4d27EucQlpaemkp5+o0G72rCm4ubnSolVn/PzuJXnTCs6ezeKLJSvx9fUhIX4xo159\nk/j4jcTERJEQv5hWbYK4cuXqbT65ZjX0qMdfOrdkz9lfKDUYFclQHQ3vbcDIlwewe/+PlJaWKR3H\nJn4NfBkxsC97DhyipEwdmRt61GN4UCv2nr5EiVr6xZAYdqf+4ND9ojZNoTjMCNzNzZW+zz3L5Ckz\nKSq6zu49B1iftIWXBkZXatundzizZn1KcXEJZ89msWjx17wyJAaAoC4BZOfksmZNEiaTieXL13Lp\nlzyei3rmbn9LFmGtGvFUq0b41LtHsQzVEd4tiLCQLvh4eykdxWZPP9mZsOBOeHt5Kh3FZmGtGxPa\nqjHernWUjmKT8O7BhIUEOXy/MFfjP7Wr1gj8+++/5/Dhw7Rq1YquXbvaNUjr1s0xGIxkZJyy7EtL\nO0pISJdbtv/9Kl0ajYZ27drc8hiABg3t27W1a14hhGOSEfhN/fr1s/x55cqVTJ06laKiIubOnXvL\nB3zeCQ93dwoKrlXYd/XqNTw93Cu13Zz8LRMnjsbDw50WLR5kyOABuLm5ArB33w80aaxnwIBIdDod\ngwb1p0WLZrjePC6EcG61aQRutYAbDAbLn1esWMGiRYt49dVXWbhwIevXr7drkMKiIrz+8Ouvl5cn\n1wqLKrV97fVJFBeXcCx9F2vXLGTFykSysi4CkJeXT9/oobz2txFcyDpIzx7d2bZtJ+fPX7RrXiGE\nYzKajTa/1M7qFIrJZOLq1auYTCbMZrPl2Wxubm5otVq7Bjlx4hQ6nZaWLR8iM/M0AI8++gjp6ccr\ntc3Pv8LLg8dYtqdNfZMD3x+0bKfs3EeXoN4AaLVaMo7v5eNP5tk1rxDCMcmt9DcVFhbSt29fzGYz\nGo2G3Nxc/Pz8KCoqsvtf0vXrxcQnfMOUyRMYMXIC/h3b8eeIHjzZLbJS2+bNm3HlSgFXrlylR3g3\nhg8bSOjTv53s9Pdvx5Ejx3F1rceUyW+QlXWB5C3f2TVvdRhMJowmM0azGZPJTKnBiNZFg87FYc4h\nV2AwGDEab75MJkpLy9Bqteh09v2hbU+Gm3lNJhMmk4nSspuZ7TzQsKdf+4XJpLZ+YXLofiG30t+0\nffv2W+53cXGxPAfOnl4d8zbzP5/NxfNpXL6cz+gxb5GefoKuwZ1IWr8Un/qtAXj88Uf5aNYUfHy8\nOZFxipcHv1rhUsMJ40fxTK9QADYn7yC6/zC7Z62O+fsymbc307K94acLjOzSkr8GtVYw1e3NW/I1\nny36yrKdlPwtsa+8wOihAxVMZV3c0jV89uVqy3bS1p3EDurHqMGO+4COz/dkMG/Pb/12Q/p5Rga1\nJrZrGytfpZx5X3zFZwuXWbaTNm8nduhARg97ScFUldWmEbg8kecP5Ik8d4c8kafm1dYn8jT2ecTm\nthevpN/x5ynJoW7kEUKIO+UMV5fYSgq4EMKpOMMt8raSAi6EcCq1aQ5cCrgQwqnUpjsxpYALIZyK\njMCFEEKl5DpwIYRQKRmBCyGESslVKEIIoVJyElMIIVRKplCEEEKl5E5MIYRQKRmBCyGEStWmOfAa\nX41QCCFEzXDMleOFEEJUSQq4EEKolBRwIYRQKSngQgihUlLAhRBCpaSACyGESkkBF0IIlVLljTwp\nKSlMnz4dk8lE//79GTFihNKRrHrrrbfYsWMHDRo0ICkpSek4Vbp48SITJ07k8uXLaDQann/+eQYP\nHqx0LKtKS0sZOHAgZWVlGI1GevbsydixY5WOVSWj0Uh0dDR6vZ558+YpHadKoaGhuLu74+Liglar\nZe3atUpHqt3MKmMwGMxhYWHmn3/+2VxaWmqOiIgwZ2RkKB3Lqv3795uPHDli7t27t9JRbJKTk2M+\ncuSI2Ww2m69du2bu0aOHw/8dm0wmc2FhodlsNpvLysrM/fr1M//3v/9VOFXVFi5caB43bpx5xIgR\nSkexyVNPPWW+fPmy0jHETaqbQklLS6NZs2Y0bdqUOnXq0Lt3b7Zt26Z0LKueeOIJvL29lY5hMz8/\nP9q1aweAh4cHzZs3JycnR+FU1mk0Gtzd3QEwGAwYDAY0Go3CqazLzs5mx44d9OvXT+koQqVUV8Bz\ncnJo1KiRZVuv1zt8cVGzrKwsfvrpJzp27Kh0lCoZjUYiIyMJCgoiKCjI4TPPmDGDN954AxcXdf0z\nHDZsGH379mXFihVKR6n11NVzxF1VVFTE2LFjefvtt/Hw8FA6TpW0Wi2JiYl89913pKWlceLECaUj\n3da3335L/fr1ad++vdJRquWrr74iPj6ezz//nGXLlnHgwAGlI9Vqqivger2e7Oxsy3ZOTg56vV7B\nRM7pxo0bjB07loiICHr06KF0nGrx8vKic+fO7Ny5U+kot/Xjjz+yfft2QkNDGTduHPv27WPChAlK\nx6rSr//WGjRoQHh4OGlpaQonqt1UV8A7dOjAmTNnOHfuHGVlZWzYsIHQ0FClYzkVs9nMO++8Q/Pm\nzXnllVeUjmOTvLw8CgoKACgpKWHPnj00b95c4VS3N378eFJSUti+fTsfffQRgYGBzJo1S+lYVl2/\nfp3CwkLLn3fv3k2rVq0UTlW7qe4yQp1Ox6RJkxg+fLjlEixH70Tjxo1j//795OfnExISwpgxY+jf\nv7/SsW7rhx9+IDExkdatWxMZGQmUfw/dunVTONnt5ebm8uabb2I0GjGbzfTq1YunnnpK6VhO5fLl\ny4wePRooP9/Qp08fQkJCFE5Vu8l64EIIoVKqm0IRQghRTgq4EEKolBRwIYRQKSngQgihUlLAhRBC\npaSACyGESkkBF0IIlfr/XvqOF10NCAIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d2a3aa780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr_df.corr(), annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wow, I do not expect this heatmap. Totally a heat!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0,\n",
       "       learning_rate=0.05, max_delta_step=0, max_depth=7,\n",
       "       min_child_weight=1, missing=None, n_estimators=10000, nthread=-1,\n",
       "       objective='reg:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=0, silent=True, subsample=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "params_xgd = {\n",
    "    'max_depth': 7,\n",
    "    'objective': 'reg:logistic',\n",
    "    'learning_rate': 0.05,\n",
    "    'n_estimators': 10000\n",
    "    }\n",
    "train_Y = close_normalize[1:]\n",
    "clf = xgb.XGBRegressor(**params_xgd)\n",
    "clf.fit(stack_predict[:-1,:],train_Y, eval_set=[(stack_predict[:-1,:],train_Y)], \n",
    "        eval_metric='rmse', early_stopping_rounds=20, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xgb_pred = clf.predict(stack_predict)\n",
    "xgb_actual = np.hstack([close_normalize[0],xgb_pred[:-1]])\n",
    "date_original=pd.Series(date_ori).dt.strftime(date_format='%Y-%m-%d').tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def reverse_close(array):\n",
    "    return minmax.inverse_transform(array.reshape((-1,1))).reshape((-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q+vG1/zK2xvRl996D9fUM7ZVAzM8/Gw1afjUiBYCSypOHOZsrsIJlsOqixqNB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\nAAACHa/loW2wdZz2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d2811e208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,6))\n",
    "x_range = np.arange(df.Close.shape[0])\n",
    "plt.plot(x_range, df.Close, label = 'Real Close')\n",
    "plt.plot(x_range, reverse_close(ada_pred), label = 'ada Close')\n",
    "plt.plot(x_range, reverse_close(bagging_pred), label = 'bagging Close')\n",
    "plt.plot(x_range, reverse_close(et_pred), label = 'et Close')\n",
    "plt.plot(x_range, reverse_close(gb_pred), label = 'gb Close')\n",
    "plt.plot(x_range, reverse_close(rf_pred), label = 'rf Close')\n",
    "plt.plot(x_range, reverse_close(xgb_pred), label = 'xgb stacked Close')\n",
    "plt.legend()\n",
    "plt.xticks(x_range[::50], date_original[::50])\n",
    "plt.title('stacked')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ada_list = ada_pred.tolist()\n",
    "bagging_list = bagging_pred.tolist()\n",
    "et_list = et_pred.tolist()\n",
    "gb_list = gb_pred.tolist()\n",
    "rf_list = rf_pred.tolist()\n",
    "xgb_list = xgb_pred.tolist()\n",
    "def predict(count, history = 5):\n",
    "    for i in range(count):\n",
    "        roll = np.array(xgb_list[-history:])\n",
    "        thought_vector = Encoder.encode(roll.reshape((-1,1)))\n",
    "        ada_pred=ada.predict(thought_vector)\n",
    "        bagging_pred=bagging.predict(thought_vector)\n",
    "        et_pred=et.predict(thought_vector)\n",
    "        gb_pred=gb.predict(thought_vector)\n",
    "        rf_pred=rf.predict(thought_vector)\n",
    "        ada_list.append(ada_pred[-1])\n",
    "        bagging_list.append(bagging_pred[-1])\n",
    "        et_list.append(et_pred[-1])\n",
    "        gb_list.append(gb_pred[-1])\n",
    "        rf_list.append(rf_pred[-1])\n",
    "        ada_actual = np.hstack([xgb_list[-history],ada_pred[:-1]])\n",
    "        bagging_actual = np.hstack([xgb_list[-history],bagging_pred[:-1]])\n",
    "        et_actual = np.hstack([xgb_list[-history],et_pred[:-1]])\n",
    "        gb_actual = np.hstack([xgb_list[-history],gb_pred[:-1]])\n",
    "        rf_actual = np.hstack([xgb_list[-history],rf_pred[:-1]])\n",
    "        stack_predict = np.vstack([ada_actual,bagging_actual,et_actual,gb_actual,rf_actual,xgb_list[-history:]]).T\n",
    "        xgb_pred = clf.predict(stack_predict)\n",
    "        xgb_list.append(xgb_pred[-1])\n",
    "        date_ori.append(date_ori[-1]+timedelta(days=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict(30, history = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lBSAjqxyA/LKGZturrqkFFDR6DS6vQpD5FII5cyAzp/EHNhuvc7XMXsei7ZNg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d281b4978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,6))\n",
    "x_range = np.arange(df.Close.shape[0])\n",
    "x_range_future = np.arange(len(xgb_list))\n",
    "plt.plot(x_range, df.Close, label = 'Real Close')\n",
    "plt.plot(x_range_future, reverse_close(np.array(ada_list)), label = 'ada Close')\n",
    "plt.plot(x_range_future, reverse_close(np.array(bagging_list)), label = 'bagging Close')\n",
    "plt.plot(x_range_future, reverse_close(np.array(et_list)), label = 'et Close')\n",
    "plt.plot(x_range_future, reverse_close(np.array(gb_list)), label = 'gb Close')\n",
    "plt.plot(x_range_future, reverse_close(np.array(rf_list)), label = 'rf Close')\n",
    "plt.plot(x_range_future, reverse_close(np.array(xgb_list)), label = 'xgb stacked Close')\n",
    "plt.legend()\n",
    "plt.xticks(x_range_future[::50], pd.Series(date_ori).dt.strftime(date_format='%Y-%m-%d').tolist()[::50])\n",
    "plt.title('stacked')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
